Interchain-expanded extra-large-pore zeolites.

Stable aluminosilicate zeolites with extra-large pores that are open through rings of more than 12 tetrahedra could be used to process molecules larger than those currently manageable in zeolite materials. However, until very recently1-3, they proved elusive. In analogy to the interlayer expansion of layered zeolite precursors4,5, we report a strategy that yields thermally and hydrothermally stable silicates by expansion of a one-dimensional silicate chain with an intercalated silylating agent that separates and connects the chains. As a result, zeolites with extra-large pores delimited by 20, 16 and 16 Si tetrahedra along the three crystallographic directions are obtained. The as-made interchain-expanded zeolite contains dangling Si-CH3 groups that, by calcination, connect to each other, resulting in a true, fully connected (except possible defects) three-dimensional zeolite framework with a very low density. Additionally, it features triple four-ring units not seen before in any type of zeolite. The silicate expansion-condensation approach we report may be amenable to further extra-large-pore zeolite formation. Ti can be introduced in this zeolite, leading to a catalyst that is active in liquid-phase alkene oxidations involving bulky molecules, which shows promise in the industrially relevant clean production of propylene oxide using cumene hydroperoxide as an oxidant.

Piscis: a novel loss estimator of the F1 score enables accurate spot detection in fluorescence microscopy images via deep learning.

Single-molecule RNA fluorescence in situ hybridization (RNA FISH)-based spatial transcriptomics methods have enabled the accurate quantification of gene expression at single-cell resolution by visualizing transcripts as diffraction-limited spots. While these methods generally scale to large samples, image analysis remains challenging, often requiring manual parameter tuning. We present Piscis, a fully automatic deep learning algorithm for spot detection trained using a novel loss function, the SmoothF1 loss, that approximates the F1 score to directly penalize false positives and false negatives but remains differentiable and hence usable for training by deep learning approaches. Piscis was trained and tested on a diverse dataset composed of 358 manually annotated experimental RNA FISH images representing multiple cell types and 240 additional synthetic images. Piscis outperforms other state-of-the-art spot detection methods, enabling accurate, high-throughput analysis of RNA FISH-derived imaging data without the need for manual parameter tuning.

Disrupting cellular memory to overcome drug resistance.

Gene expression states persist for varying lengths of time at the single-cell level, a phenomenon known as gene expression memory. When cells switch states, losing memory of their prior state, this transition can occur in the absence of genetic changes. However, we lack robust methods to find regulators of memory or track state switching. Here, we develop a lineage tracing-based technique to quantify memory and identify cells that switch states. Applied to melanoma cells without therapy, we quantify long-lived fluctuations in gene expression that are predictive of later resistance to targeted therapy. We also identify the PI3K and TGF-ß pathways as state switching modulators. We propose a pretreatment model, first applying a PI3K inhibitor to modulate gene expression states, then applying targeted therapy, which leads to less resistance than targeted therapy alone. Together, we present a method for finding modulators of gene expression memory and their associated cell fates.

Constructing Intrapenetrated Hierarchical Zeolites with Highly Complete Framework via Protozeolite Seeding.

Creation of intrapenetrated mesopores with open highway from external surface into the interior of zeolite crystals are highly desirable that can significantly improve the molecular transport and active sites accessibility of microporous zeolites to afford enhanced catalytic properties. Here, different from traditional zeolite-seeded methods that generally produced isolated mesopores in zeolites, nanosized amorphous protozeolites with embryo structure of zeolites were used as seeds for the construction of single-crystalline hierarchical ZSM-5 zeolites with intrapenetrated mesopores (mesopore volume of 0.51 cm3 g-1 ) and highly complete framework. In this strategy, in contrast to the conventional synthesis, only a small amount of organic structure directing agents and a low crystallization temperature were adopted to promise the protozeolites as the dominant growth directing sites to induce crystallization. The protozeolite nanoseeds provided abundant nucleation sites for surrounding precursors to be crystallized, followed by oriented coalescence of crystallites resulting in the formation of intrapenetrated mesopores. The as-prepared hierarchical ZSM-5 zeolites exhibited ultra-long lifetime of 443.9 hours and a high propylene selectivity of 47.92 % at a WHSV of 2 h-1 in the methanol-to-propylene reaction. This work provides a facile protozeolite-seeded strategy for the synthesis of intrapenetrated hierarchical zeolites that are highly effective for catalytic applications.

Ultrasmall water-stable CsPbBr3 quantum dots with high intensity blue emission enabled by zeolite confinement engineering.

Ultrasmall CsPbBr3 perovskite quantum dots (PQDs) as promising blue-emitting materials are highly desired for full-color display and lighting applications, but their inferior efficiency and poor ambient stability hinder extensive applications. Herein, a "break-and-repair" strategy has been developed to tightly confine monodispersed ultrasmall CsPbBr3 PQDs in a zeolite. In this strategy, the CsPbBr3 PQDs are introduced into the zeolite via a high temperature evaporation method, wherein the perovskite precursors break the zeolite framework, and amino acids and silane are then used to fix the damaged framework and lock the perovskite QDs within the matrix. By modulating the synthetic conditions to control the growth of CsPbBr3, PQDs with ultrasmall size of 2 nm have been obtained in the zeolite, giving emission centered at 460 nm with a high quantum yield of 76.93%. Strikingly, the PQDs@zeolite composite exhibits water-induced reversible photoluminescence promoted by the coordination between the amino acids and PQDs in a dynamic manner, achieving enhanced water stability (14 days in aqueous solution). This work provides a new perspective for the synthesis of water-stable blue-emitting perovskite composites for potential applications in lighting fields.

Regioselective Surface Assembly of Mesoporous Carbon on Zeolites Creating Anisotropic Wettability for Biphasic Interface Catalysis.

The anisotropic surface functionalization of microporous zeolites with mesoporous materials into hierarchically porous heterostructures with distinctive physical and chemical properties is expected to significantly extend their applicability to catalysis. However, the precise control of the surface chemistry of zeolite crystals through site-specific interconnection with mesoporous materials remains a grand challenge. Here, we report a regioselective surface assembly strategy for the region-specific growth of mesoporous polymer/carbon on zeolite nanocrystals. The approach enables controllable regioselective surface deposition of mesoporous polydopamine on the edges, curved surfaces, or/and flat surfaces of the silicalite-1 nanocrystals into exotic hierarchical nanostructures with diverse surface geometries. Upon carbonization, their derived heterostructures with anisotropic surface wettability show amphiphilic properties. As a proof of concept, Pt nanoparticle-encapsulated silicalite-1/mesoporous carbon nanocomposites are tested to be interface-active for forming Pickering emulsions. Significantly, the catalysts show superior catalytic performance in shape-selective hydrogenation of various nitroarenes in a series of biphasic tandem catalytic reactions, giving ∼100% yield of corresponding amine products. The results pave a path toward rational construction of high levels of surface structural complexity in hierarchically porous heterostructures for specific physical and chemical characteristics in diverse applications.

Detection of Alternaria solani with high accuracy and sensitivity during the latent period of potato early blight.

Early blight (EB) disease, caused mainly by Alternaria solani, is an economic threat to potato and tomato production worldwide. Thus, accurate and sensitive detection of the fungal pathogen of this disease in plants at the early infection stage is important for forecasting EB epidemics. In this study, we developed an RNA-based method that enables highly accurate and sensitive A. solani detection in a whole potato leaf at a single spore level based on quantitative real-time polymerase chain reaction (qPCR). We discovered jg1677, a highly expressed gene whose full-length coding sequence is very specific for A. solani, by analyzing A. solani transcripts isolated from enhanced high throughput transcriptome of infected potato leaves by A. solani and using the National Center for Biotechnology Information's basic local alignment search tool. The specificity of the primers derived from jg1677 was determined using 22 isolates of common potato pathogens, including seven Alternaria isolates. Detecting jg1677 transcripts with qPCR is 1,295 times more sensitive than detecting genomic DNA. In addition, the expression pattern of jg1677 at different infection stages was determined by qPCR. What is more, jg1677 was expressed relatively stable between 15 and 35°C in infected leaves, and its expression was virtually unaffected in isolated leaves left at room temperature for 24 h. Our work provides a much more sensitive and accurate method compared to conditional DNA-based ones, permitting a very early diagnosis of EB and lowering the risk of EB epidemics.

Single-Nucleotide Polymorphism LncRNA AC008392.1/rs7248320 in CARD8 is Associated with Kawasaki Disease Susceptibility in the Han Chinese Population.

BACKGROUND: Kawasaki disease (KD) is a multisystem vasculitis in infants and young children and involved in the NOD-like receptor family, pyrin domain-containing 3 (NLRP3) inflammasome activation. Genetic factors may increase the risk of KD. To assess the association between rs7248320 in long noncoding RNA (lncRNA) AC008392.1 located in the upstream region of CARD8 and the risk of KD, a case-control study was conducted in the Han Chinese population. METHODS: This study genotyped the polymorphism rs7248320 in the lncRNA AC008392.1 gene using the TaqMan real-time polymerase chain reaction assay. The genetic contribution of rs7248320 was evaluated using odds ratios (ORs) and 95% confidence intervals (CIs) using unconditional logistic regression analysis. The association between rs7248320 and KD susceptibility was analyzed by performing a hospital-based case-control study including 559 KD patients and 1055 non-KD controls. RESULTS: In this study, a significant relationship between rs7248320 and KD risk was observed in the genotype/allele frequency distribution. The rs7248320 polymorphism was associated with a significantly decreased risk of KD after adjustment for age and sex (AG vs AA: adjusted OR = 0.80, 95% CI: 0.64-0.99, P = 0.0421; GG vs AA: adjusted OR = 0.71, 95% CI: 0.51-1.00, P = 0.0492; AG/GG vs AA: adjusted OR = 0.78, 95% CI: 0.63-0.96, P = 0.0186). Moreover, the rs7248320 G allele also exhibited a decreased risk for KD (adjusted OR = 0.83, 95% CI: 0.72-0.97, P = 0.0193) compared with the A allele. In the stratification analysis, compared to the rs7248320 AA genotype, AG/GG genotypes were more protective for males (OR = 0.71, 95% CI: 0.55-0.93, P = 0.0122). CONCLUSION: This study suggests for the first time that the lncRNA AC008392.1 rs7248320 polymorphism may be involved in KD susceptibility in the Han Chinese population.

LSTM-Based VAE-GAN for Time-Series Anomaly Detection.

Time series anomaly detection is widely used to monitor the equipment sates through the data collected in the form of time series. At present, the deep learning method based on generative adversarial networks (GAN) has emerged for time series anomaly detection. However, this method needs to find the best mapping from real-time space to the latent space at the anomaly detection stage, which brings new errors and takes a long time. In this paper, we propose a long short-term memory-based variational autoencoder generation adversarial networks (LSTM-based VAE-GAN) method for time series anomaly detection, which effectively solves the above problems. Our method jointly trains the encoder, the generator and the discriminator to take advantage of the mapping ability of the encoder and the discrimination ability of the discriminator simultaneously. The long short-term memory (LSTM) networks are used as the encoder, the generator and the discriminator. At the anomaly detection stage, anomalies are detected based on reconstruction difference and discrimination results. Experimental results show that the proposed method can quickly and accurately detect anomalies.